1. Field of the Invention
The present invention relates to vehicle navigation systems and more particularly to a vehicle navigation system enabled to predict a vehicle destination and/or route.
2. Description of the Background Art
There is currently a need for vehicle navigation systems to be able to make predictions regarding, for example, a vehicle's destination and/or route to the destination. The customer experience of a system that predicts its action is highly dependent on the reliability of the predictions. The predictions must be accurate and quickly available.
Typically, such predictions are determined based on comparing a current trip with recorded trip patterns driven in the past. If the current trip matches a historic trip pattern, then the destination/route of the historic trip is proposed to the user. The likelihood of historic trips are calculated based on comparing the current location and/or heading and the current time (e.g., time of day, day of week, etc.) to the recorded trip patterns. Instead of considering just the current position, these mechanisms take the driven route so far into account (or a part of it like the last mile) and compare it to the recorded trip pattern.
For example, U.S. Patent Application Publication No. 2013/0166096 discloses a predictive destination entry system for a vehicle navigation system. The system uses a memory for storing data related to prior driving history or habits. The system makes vehicle destination predictions based on the historical driving data.
Somewhat similarly, WO 2007/067842 discloses a vehicle navigation system that saves addresses corresponding to vehicle destinations along with parameters related to the addresses. The navigation system uses the parameters to predict a destination by comparing the present state of the vehicle to the saved parameters. The system also considers traffic conditions in determining possible routes to the predicted destination.
Furthermore, U.S. Pat. No. 7,233,861 discloses a system for predicting vehicle destinations. The system compares current vehicle position data to vehicle position data for a previous trip to predict a destination for the vehicle. The system then proposes a route to the predicted destination.
Additionally, U.S. Pat. No. 7,487,017 discloses a predictive navigation system. The system obtains route information traveled by the vehicle. The route information includes a starting location, a destination location, and one or more decision points along the route. The route information is used to create a neural network, which the system then uses to predict current potential routes.
Most trips, however, begin at the same location (e.g. home, work) going to the same direction (e.g. freeway entrance) often at about the same time (e.g. leaving home in the morning, heading to the freeway). If only time and location are used, the system cannot distinguish between destinations/routes that are usually driven at the same time and begin with the same path. Until an intersection or exit is reached where the paths differ, the system might decide to display just a most likely destination/route (e.g., the destination/route that was driven most often). This approach, however, can lead to incorrect predictions being shown for a long time and, therefore, lead to poor results and limited user benefit. The above conventional techniques each suffer from this problem.
Certain conventional techniques have addressed this problem by predicting destinations based on searches that were conducted in the past (e.g., this is the Google Now approach). If a user searched for “gas station” or “best deal on gas in San Mateo,” then the system will predict suitable destinations in the area. Other conventional techniques connect to a user's mobile phone or Internet device to access the user's calendar or address book to find information regarding a next likely destination. Similarly, some systems access online event calendars and match events in the calendar with the user's interests to predict likely destinations.
Furthermore, certain known approaches have a limited ability to take into account current fuel/energy levels or remaining fuel/energy range when predicting destinations/routes. This information is used to suppress predictions that are not in the current range. If the car is low on fuel, then the system can propose likely routes to gas stations to user (i.e., predicting the situation “fill up the car.”) Other situations, however, cannot be distinguished and are not taken into account when determining the predictions.